Nsoffice
Written by the Bee, read it before you send it
KPIMore first meetings turn into real deals, on less preparation time
selling on-prem storage
Since NSOffice.AI onboarded 22 enterprise customers and scaled to 100+ connectors post-launch, the underlying storage footprint needed to keep each customer's data governed and siloed has grown fast.
On-prem storage infrastructure
Private-cloud deployment options with GDPR-compliant, non-training data handling high
Supporting rollout to NSOffice.AI's onboarded enterprise customers
Bhargav Petla
Ai-fication Specialist at Network Science (parent company of NSOffice.AI) · Previously AI Consultant at Network Science; prior roles at Dell Technologies, Orion Square, and Verzeo
Bhargav Petla works on AI enablement at Network Science, the deep-tech firm that built and launched NSOffice.AI as an enterprise AI workspace product. He is named among the core team credited by colleagues at the NSOffice.AI launch event (DTIA 10).
- Supporting rollout to NSOffice.AI's onboarded enterprise customers
- Driving enterprise AI adoption while managing governance/shadow-AI risk
- Positioning NSOffice.AI's multi-LLM orchestration and integrations to prospects
- Building on the recent NSOffice.AI product launch momentum
- Enterprise AI adoption and "shadow AI" governance
- Multi-LLM orchestration for enterprise workflows
- Team culture and offsites at Network Science
- AI-generated content creation using NSOffice.AI internally
Colleague post noting "6 years of Network Science" tenure marker tied to Petla, referencing a recent team offsite
Credited by a colleague in a post about the NSOffice.AI unveiling at DTIA 10
Nsoffice today
- Industry
- Enterprise AI / deep tech software
- Size
- Small-to-mid enterprise software vendor; 22 enterprise customers onboarded since launch
- HQ
- Mumbai, India (also presence in London, UK)
- Footprint
- India (Mumbai) · United Kingdom (London) · Serves 100+ global enterprise relationships
NSOffice.AI, built by Network Science, is an enterprise AI workspace positioned as a "single ask bar" that unifies search, task automation, and multi-LLM orchestration across business systems. The company markets itself as replacing fragmented enterprise tools with one governed, contextual AI platform, targeting large enterprises concerned about data governance.
- May 2026: Published 'Introducing NSOffice.AI' launch video on YouTube
- Publicly unveiled NSOffice.AI at DTIA 10 industry event
- Beta launch of NSOffice.AI announced via LinkedIn (Sandy Hardikar post)
- Since launch, onboarded 22 enterprise customers per BOLD Awards project listing
- Markets against 'shadow AI' risk, implying customers using ungoverned AI tools today
- Site states executives spend 23 hours weekly hunting across systems for information
- Positions against employees sharing sensitive data with LLMs discreetly, a stated liability
- Public commitment to GDPR-compliant, non-training data handling as a governance differentiator
- Built 100+ connectors to avoid 'rip-and-replace' integration with existing enterprise stacks
- Multi-LLM orchestration strategy partnering with 'best-in-class LLM models' rather than building proprietary models
Positioning
We provide the on-prem storage infrastructure that lets you host governed, non-training-data AI workloads entirely within a customer's own environment. That's the physical backbone behind the private-cloud and data residency promises NSOffice.AI makes to its enterprise buyers.
Pitches
On-prem storage infrastructure
Gives you a controllable, auditable storage layer to make good on NSOffice.AI's non-training, data-residency promises without relying on third-party cloud assurances. This directly supports the governance credibility you're building with enterprise buyers.
On-prem storage for private-cloud deployments
Speeds up your 4-6 week deployment cycle by removing storage provisioning as a variable when onboarding new enterprise customers. Keeps each customer's environment isolated exactly as your private-cloud deployment option claims.
Conversation arc
“Bhargav, given NSOffice.AI's push on private-cloud deployment and non-training data handling, I wanted to talk about the on-prem storage layer underneath those promises.”
“How are you currently handling data residency and storage isolation across your 22 onboarded enterprise customers?”
“Our on-prem storage lets you back your GDPR-compliant, non-training claims with infrastructure you fully control, not just a policy statement.”
“I hear you on added infrastructure overhead — let's look at how this scales alongside your connector and customer growth rather than in front of it.”
“Can we set up a technical walkthrough with your infrastructure team to map this against your current private-cloud deployments?”
Ask these
- Q1How are you managing data residency and isolation storage across your 100+ connectors spanning ERP, CRM, and HRMS systems?
- Q2With 22 enterprise customers onboarded, is your current storage architecture keeping pace with that governed private-cloud commitment?
- Q3Given your public focus on shadow-AI governance, does your team see storage control as part of that risk story or a separate workstream?
- Q4As deployments run on a 4-6 week onboarding timeline, is storage provisioning ever a bottleneck in hitting that window?
Feasibility
What they appear to run
- Enterprise page states NSOffice 'partners with best-in-class LLM models to orchestrate a secure interface'
- Homepage lists 100+ connectors spanning ERP & Finance, CRM & Sales, HRMS, BI & Analytics, Document & Storage
- BOLD Awards listing confirms RBAC, private-cloud deployment, SSO, and data residency controls are live features